Campus professors are using their research to better understand COVID-19 statistics, despite challenges with collecting health data during a pandemic.

UC Berkeley professors discussed their work on collecting and using COVID-19 infection data as a part of the “Berkeley Conversations: COVID-19” series. The event, which was sponsored by the campus Division of Computing, Data Science and Society and the Berkeley Institute for Data Science, was led by campus physics professor Saul Perlmutter.

Each of the participating three professors discussed their research, much of which was begun before the COVID-19 pandemic, in great detail, explaining a little bit about how each of their models worked.

Campus assistant professor of statistics Jacob Steinhardt showed his models studying overall infection rates, where the highest numbers of COVID-19 cases are and how the transmission of the virus can be understood. He said he was also looking at the data’s reliability and how it could be used to determine policy.

Steinhardt added that the shelter-in-place orders have decreased community transmission of the viruses but increased the percentage of household transmissions from 10% to 35% of total transmission.

According to Steinhardt, finding exact numbers has been complicated, but collecting more qualitative data, such as the age of a population and where people work, has been helpful. He said he hopes hospitals start asking their patients where they work and for other information about them in order to grow this dataset.

Campus physics professor Uros Seljak also shared his work, discussing his use of mortality data in Italy to understand the coronavirus’s mortality rates around the world. He found that mortality rates of COVID-19 per age group closely resemble the statistical odds that a person dies within the next six months by other causes.

“What COVID does is it packs this whole (chance of dying) into a couple of weeks rather than 12 months,” Seljak said during the event.

He added that some of the data is unreliable because, often, only the sickest people are tested for the disease.

Campus professor of electrical engineering and computer science Shafi Goldwasser shared her research into technology and software that can use private data to reach conclusions without actually reading or sharing it.

She added that this is especially important now, when a large amount of private health data that many do not want to or cannot share due to privacy laws is needed to understand the spread of disease better.

“They’re great examples of what is it that a major university has going on as a starting point,” Perlmutter said during the event. “When we see an epidemic like this, we’re jumping in and we’re able to do things that will start becoming crucial very, very quickly.”